The present invention relates to an image processor for digital-analyzing various gray-level images to obtain required image information and particularly to an image processor which can automatically extract, accurately and promptly, the boundary of images of organs or the like or other image information from images such as X-ray projections of a part of a living body or the like which are extremely low in resolution and low in contrast.
In the computer tomography (CT) which has recently achieved a great effect in medical diagnosis, it becomes possible to obtain a tomographic image of a living body with high resolution, which has not been obtainable with conventional X-ray images, by analysis by use of a computer. However, in the CT device, the living body must be scanned by X-rays at least for a few seconds to obtain a single tomographic image. This means that it is absolutely impossible for the CT device to obtain an image, for example, of a heart or the like which normally repeats its pulsation at short intervals of, say, about one second. Under these circumstances, the movement of the heart has been subjected to continuous projection under X-ray irradiation to obtain X-ray projections required for diagnosis. However, since the projections thus obtained have low resolution, it has not been possible to successfully obtain an accurate boundary of an organ or image information required for various diagnoses. Thus, for example, when an accurate boundary of the image is required, the boundary has been reproduced by tracing the image by hand. This method, however, has disadvantages in that the boundary is imprecise and it takes excessive time to obtain the boundary.
There has been proposed a subtraction method of extracting a moving part of image from the difference between successive two frames obtained from the image signal from a television camera. In this method, however, it is difficult to obtain less-movable parts of images to which a sufficiently great differential signal is not applied, for example, such as a portion in the vicinity of an aortic valve, a portio infract, etc.
Other methods of extracting a boundary of an image so far proposed include a method of primary-differentiating the global image to use a portion having a large value as a boundary (Fujimura, "Line Depiction of Edges of a Gray-level Figure by Local Parallel Processing", in Japanese, Information Processing, 17-7, p. 625, July 1976) and a method using a secondary differentiation instead of the primary differentiation (Sakai et al. "Processing of Gray-level Images by Electronic Computer - Photograph of Face", in Japanese, JIEC (C), 54-C, 6, p. 445, June 1971). A further proposal of a method for the local extraction by differentiation has been made by A. Rosenfelt, ("A Nonlinear Edge Detection Technique" Proc. IEEE, 58-5, p. 814, May 1970.) Since such image processing methods by differentiation depend on local information in the image, it is liable to be affected by noise in the image and to disadvantageously lose the global information.
Another image processing method has been proposed which comprises setting a certain threshold on the basis of desnity of the global image, converting the gray-level at each point of the image into binary data, and extracting a binary boundary in the binary image as a contour. This image thresholding method can be classified depending on the way for establishing the threshold. One method proposed for establishing the threshold comprises dividing the global image plane into small regions, approximating a histogram in each small region by the sum of two normal distributions, and setting a threshold by a statistical procedure thereby to extract a boundary (C. K. Chow & T. Kaneko, "Automatic Boundary Detection of the Left Ventricle from Cineangiograms", Computers & Biomedical Research 5, pp 388-410, 1972). While this method positively uses a statistical theory and is considered to be ideal, it nevertheless poses various problems as described below. First, the gray-level histogram of an image is not always approximated by two normal distributions. The conditions for thresholding are determined only by the values of two peaks and a valley on the histogram and are unstable. Since the boundary is extracted only by pointwise thresholding of gray-levels of the image, it is liable to be affected by noise, and the boundary becomes complicated and inaccurate. It is useless to apply thresholds to all the small regions for the binary thresholding. There is a disadvantage that it requires much computation time for estimating parameters of normal distributions.
Studies on the extraction of boundaries in medical image processing further include reports from Suenaga et al, "Range Filter for Detection of Variation in Local Density of Gray-Level Image" (JIEC, A71-105, Jan. 1972); Fukushima et al, "Extraction of Edge of X-ray Projection of Stomach" (Medical Electronics and Bio-engineering, Vol. 15, No. 6, pp. 7-, Oct. 1977); and Yamura et al, "Extraction of Left Ventricular Contour from Projected Image of RI Blood-Vessel" (Medical Electronics and Bio-engineering, Vol. 14, No. 6, pp 452-, Dec. 1976). These are also roughly divided into a local procedure by differentiation or tracing and a global procedure by thresholds of gray-level, as described hereinbefore. These procedures involve points which are insufficient for clinical application since the former is liable to be affected by noise and prevented from obtaining the global information and the latter is not easy in determination of the threshold and has not established a way which can cope with shaded images and with complicated images.